"""
v0 script written by Myriam Benisty and Stefano Facchini (Feb 2st 2022)
v1 version adapted by Stefano Facchini and Myriam Benisty (Mar 2nd 2022)
v2 version adapted to include ACA data by Jane Huang and compacted code by Gianni Cataldi (May 19th 2022)
v3 (current version) includes EB alignment by Ryan Loomis and Richard Teague (Aug 2022)


This script is written for CASA 6.2.1.7. 
It can easily be adapted to other versions of CASA, e.g. by modifying the fixvis task to phaseshift task.

The numba package is used for the alignment script, but it is not necessary (it helps with the speed-up)

The scripts are written to be compatible mpicasa.
However, after testingthe exoALMA collaboration decided not to use mpicasa in any of the calibration scripts,
since they realized that differences in the results between casa and mpicasa could arise.

Person in charge for this source:
G. Cataldi

"""

import os
import numpy as np
import shutil
import matplotlib
#increase the number of figures that can be open before issuing a warning
matplotlib.rcParams.update({'figure.max_open_warning':80})

#path to your local copy of the gihub repository
#(make sure to do a 'git pull' to have the most up-to-date version)
github_path = '/users/gcataldi/calibration_scripts'
import sys
sys.path.append(github_path)
import alignment
execfile(os.path.join(github_path,'reduction_utils_exoalma.py'))

prefix = 'CQ_Tau'

data_folderpath = '/lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/CQ_Tau'
TM_names = {'LB':'TM1','SB':'TM2'}
PL_calibrated_vis = {key:os.path.join(data_folderpath,f'{TM_name}/calibrated_final.ms')
                     for key,TM_name in TM_names.items()}

for baseline_key,TM_name in TM_names.items():
    listobs(
        vis=PL_calibrated_vis[baseline_key],
        listfile=f'{prefix}_{TM_name}_calibrated_final.ms.txt',
        overwrite=True,
    )


# System properties.
incl =  35. # Orihara in prep.
PA = 235   # deg
v_sys = 6.2 # km/s; use the one from listobs

# Whether to run tclean in parallel or not.
use_parallel = True

fields = {'SB':'CQ_Tau','LB':'CQ_Tau'}

number_of_EBs = {'SB':2,'LB':4}

for baseline_key,vis in PL_calibrated_vis.items():
    split_all_obs(msfile=vis,nametemplate=f'{prefix}_{baseline_key}_EB')

for baseline_key,n_EB in number_of_EBs.items():
    for i in range(n_EB):
        vis = f'{prefix}_{baseline_key}_EB{i}.ms'
        listobs(
            vis=vis,
            listfile=f'{vis}.txt',
            overwrite=True,
        )

"""
Check that spws and field numbering is what you expect them to be now from the listobs files
In particular, make sure that there are 4 spws and that there is a single field with
field ID = 0
"""

# Line rest frequencies from splatalogue (https://splatalogue.online)
rest_freq_12CO = 345.79598990e9 #J=3-2
rest_freq_13CO = 330.58796530e9 #J=3-2 (ignoring splitting)
rest_freq_CS   = 342.88285030e9 #J=7-6

data_params_LB = {f'LB{i}': {'vis' : f'{prefix}_LB_EB{i}.ms',
                             'name' : f'LB_EB{i}',
                             'field' : fields['LB'],
                             'line_spws': np.array([0,2,3]), # list of spws containing lines
                             'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]), #frequencies (Hz) corresponding to line_spws
                             'cont_spws': None,
                             'width_array': None,
                             }
                  for i in range(number_of_EBs['LB'])}

data_params_SB = {f'SB{i}': {'vis' : f'{prefix}_SB_EB{i}.ms',
                             'name' : f'SB_EB{i}',
                             'field' : fields['SB'],
                             'line_spws': np.array([0,2,3]), # list of spws containing lines
                             'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]), #frequencies (Hz) corresponding to line_spws
                             'cont_spws': None,
                             'width_array': None,
                             }
                  for i in range(number_of_EBs['SB'])}


data_params = data_params_LB.copy()
data_params.update(data_params_SB)

figures_foldername = 'figures'
os.mkdir(figures_foldername)

def get_figures_folderpath(foldername):
    return os.path.join(figures_foldername,foldername)

def make_figures_folder(folderpath):
    if os.path.isdir(folderpath):
        print(f'going to deleted folder {folderpath} and its content')
        valid_answer = 'yes'
        answer = input(f'to confirm, type \'{valid_answer}\': ')
        if answer == valid_answer:
            shutil.rmtree(folderpath)
        else:
            print('aborting')
            return
    os.mkdir(folderpath)
    return folderpath

preselfcal_amp_figures_folder = get_figures_folderpath('1_preselfcal_amp_figures')
make_figures_folder(preselfcal_amp_figures_folder)

#adjust these plot ranges according to your data
plotranges = {'SB':[0,1000,0,0.3],#xmin,xmax,ymin,ymax
              'LB':[0,3500,0,0.3]}

for params in data_params.values():
    plot_filename = prefix+'_'+params['name']+'_chan-v-amp_preselfcal.png'
    plotms(vis=params['vis'],
            xaxis='channel',
            yaxis='amplitude',
            field=params['field'],
            ydatacolumn='data',
            avgtime='1e8',
            avgscan=True,
            avgbaseline=True,
            iteraxis='spw',
            #There is a bug in plotms that plots the data wrongly if there is unequal flagging
            #between the polarisations
            #the bug occurs if we plot amp and we average over baselines
            #thus we color by polarization such that
            #we easily identify this issue
            coloraxis='corr', 
            showgui = False,
            exprange='all',
            plotfile=os.path.join(preselfcal_amp_figures_folder,plot_filename)
            )
    baseline_key,_ = params['name'].split('_')
    plotms(vis=params['vis'],
           xaxis='UVdist',
           yaxis='amplitude',
           spw='1',
           field=params['field'],
           ydatacolumn='data',
           avgtime='1e8',
           avgscan=True,
           avgchannel='3840',
           showgui = False,
           plotrange=plotranges[baseline_key],
           plotfile=os.path.join(preselfcal_amp_figures_folder,
                                 prefix+'_'+params['name']+'_uvdist-v-amp_cont_spw_preselfcal.png')
           )

#for CQ Tau, the plotms polarisation bug seems indeed there in LB EB1 spw3, so let's plot
#polarizations separately to verify
for corr in ('XX','YY'):
    plot_filename = prefix+'_'+data_params['LB1']['name']\
                         +f'_chan-v-amp_preselfcal_pol{corr}_spw3.png'
    plotms(vis=data_params['LB1']['vis'],
            xaxis='channel',
            yaxis='amplitude',
            field=data_params['LB1']['field'],
            ydatacolumn='data',
            avgtime='1e8',
            avgscan=True,
            avgbaseline=True,
            spw='3',
            correlation=corr,
            showgui = False,
            exprange='all',
            plotfile=os.path.join(preselfcal_amp_figures_folder,plot_filename)
            )
#when plotting polarizations separately, everything looks fine, so it's indeed just the
#plotms bug

for params in data_params.values():
    flagchannels_string = get_flagchannels(ms_dict=params,output_prefix=prefix,
                                           velocity_range=np.array([-15.,15.])+v_sys)
    avg_cont(ms_dict=params,output_prefix=prefix,flagchannels=flagchannels_string,
             contspws=params['cont_spws'],width_array=params['width_array'])

"""
Double-check that the channels idenfied are at the center of the spws,
due to a potential issue with the data_desc_id key in the ms table of some programs
"""
# Flagchannels input string for LB_EB0: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB1: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB2: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB3: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for SB_EB0: '0:833~3001, 2:797~3046, 3:780~3048'
# Flagchannels input string for SB_EB1: '0:833~3001, 2:797~3046, 3:781~3048'


preselfcal_initcont_amp_folder = get_figures_folderpath(
                                  '2_preselfcal_initcont_amp_figures')
make_figures_folder(preselfcal_initcont_amp_folder)

uv_ranges = {'LB':'125~150m','SB':'125~150m'}

for params in data_params.values():
    vis = prefix+'_'+params['name']+'_initcont.ms'
    baseline_key = params['name'].split('_')[0]
    plotms(vis=vis,
            xaxis='UVdist',
            overwrite=True,
            yaxis='amp',
            coloraxis='spw',
            avgtime='1e8',
            avgscan=True,
            showgui=False,
            plotrange=plotranges[baseline_key],
            plotfile=os.path.join(preselfcal_initcont_amp_folder,
                                  prefix+'_'+params['name']+'_uvdist-v-amp_initcont_preselfcal.png'))

    plotms(vis=vis,
            xaxis='time',
            yaxis='amp',
            avgspw=True,
            uvrange=uv_ranges[baseline_key],
            avgchannel='10000',
            avgbaseline=True,
            coloraxis='corr',
            showgui=False,
            overwrite=True,
            plotfile=os.path.join(preselfcal_initcont_amp_folder,
                                  prefix+'_'+params['name']+'_amp_v_time_initcont_preselfcal.png')
            )
#for SB, we see "waterfall features" (times where the amp suddently sharply decreases)
#we will try to fix this with self-cal


preselfcal_images_folder = get_figures_folderpath('3_preselfcal_images')
make_figures_folder(preselfcal_images_folder)


""" Define simple masks and clean scales for imaging """
mask_pa = PA #position angle of mask in degrees
mask_semimajor = 1.8 #semimajor axis of mask in arcsec
mask_semiminor = mask_semimajor*np.cos(incl/180.*np.pi) #semiminor axis of mask in arcsec
mask_ra = '05h35m58.470924s' #taken from listobs
mask_dec = '+24.44.53.50573'

mask = f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
            + f' {mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'
# Cellsize: ~beam/6-7
cellsize =  {'LB':'0.015arcsec','SB':'0.040arcsec'}
# Image size: ~primary beam 1.22*lam/A = 32'' with A=12m (19 arcsec)
imsize = {'LB':1200,'SB':400}# primary beam
scales = {'LB':[0,8,15,30,80],'SB':[0,8,15,30]}

noise_annulus = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"
thresholds = {'LB':'0.6mJy','SB':'12mJy'} #clean down to 6 sigma for phase cal

image_png_plot_sizes = [3,10] #sizes in arcsec of the zoomed and overview plots of the pngs


for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for EB_key,p in params.items():
        imagename = prefix+'_'+p['name']+'_initcont_image'
        delete_tclean_output(imagename)
        tclean_wrapper(
                      vis=prefix+'_'+p['name']+'_initcont.ms',
                      imagename=imagename,
                      deconvolver='multiscale',
                      scales=scales[baseline_key],
                      mask=mask,
                      threshold=thresholds[baseline_key],
                      cellsize=cellsize[baseline_key],
                      imsize=imsize[baseline_key],
                      parallel=use_parallel,
                      savemodel='modelcolumn'
                    )
        estimate_SNR(f'{imagename}.image',disk_mask=mask,noise_mask=noise_annulus)
        rms = imstat(imagename=f'{imagename}.image',region=noise_annulus)['rms'][0]
        params[EB_key]['rms'] = rms
        generate_image_png(f'{imagename}.image',plot_sizes=image_png_plot_sizes,
                            color_scale_limits=[-3*rms,10*rms],
                            save_folder=preselfcal_images_folder,
                            mask=f'{imagename}.mask',noise_annulus=noise_annulus)

#CQ_Tau_LB_EB0_initcont_image.image
#Beam 0.106 arcsec x 0.065 arcsec (-32.80 deg)
#Flux inside disk mask: 429.12 mJy
#Peak intensity of source: 10.97 mJy/beam
#rms: 9.93e-02 mJy/beam
#Peak SNR: 110.56

#CQ_Tau_LB_EB1_initcont_image.image
#Beam 0.111 arcsec x 0.070 arcsec (-35.64 deg)
#Flux inside disk mask: 441.10 mJy
#Peak intensity of source: 12.29 mJy/beam
#rms: 1.05e-01 mJy/beam
#Peak SNR: 117.10

#CQ_Tau_LB_EB2_initcont_image.image
#Beam 0.102 arcsec x 0.073 arcsec (-30.58 deg)
#Flux inside disk mask: 453.46 mJy
#Peak intensity of source: 11.92 mJy/beam
#rms: 1.13e-01 mJy/beam
#Peak SNR: 105.31

#CQ_Tau_LB_EB3_initcont_image.image
#Beam 0.096 arcsec x 0.073 arcsec (-15.57 deg)
#Flux inside disk mask: 439.27 mJy
#Peak intensity of source: 11.34 mJy/beam
#rms: 8.46e-02 mJy/beam
#Peak SNR: 133.96

#CQ_Tau_SB_EB0_initcont_image.image
#Beam 0.629 arcsec x 0.483 arcsec (-12.42 deg)
#Flux inside disk mask: 369.56 mJy
#Peak intensity of source: 153.97 mJy/beam
#rms: 1.86e+00 mJy/beam
#Peak SNR: 82.96

#CQ_Tau_SB_EB1_initcont_image.image
#Beam 0.629 arcsec x 0.472 arcsec (-14.10 deg)
#Flux inside disk mask: 361.04 mJy
#Peak intensity of source: 138.21 mJy/beam
#rms: 1.96e+00 mJy/beam
#Peak SNR: 70.52


######
# SELF-CAL INDIVIDUAL EBs
######
""" Self-calibration parameters """
single_EB_contspws = '0~3'
single_EB_spw_mapping = [0,0,0,0]

individual_EB_selfcal_folder = get_figures_folderpath(
                                                   '4_individual_EB_selfcal_figures')
make_figures_folder(individual_EB_selfcal_folder)

""" One round of phase-only self-cal """
for params in data_params.values():
    vis = prefix+'_'+params['name']+'_initcont.ms'
    single_EB_p1 = prefix+'_'+params['name']+'_initcont.p1'
    os.system(f'rm -rf {single_EB_p1}')
    gaincal(vis=vis,caltable=single_EB_p1,gaintype='T',spw=single_EB_contspws,
            combine='scan,spw',calmode='p',solint='inf',minsnr=4,minblperant=3)
    """ Print calibration png file """
    plotfilename = prefix+'_'+params['name']\
                        +'_initcont_gain_p1_phase_vs_time.png'
    plotms(single_EB_p1,xaxis='time',yaxis='GainPhase',overwrite=True,showgui=False,
           plotfile=os.path.join(individual_EB_selfcal_folder,plotfilename))
    """ Apply the solutions """
    applycal(vis=vis,spw=single_EB_contspws,spwmap=single_EB_spw_mapping,
             gaintable=[single_EB_p1],interp='linearPD',applymode='calonly',calwt=True)
    outputvis = prefix+'_'+params['name']+'_initcont_selfcal.ms'
    os.system(f'rm -rf {outputvis}')
    split(vis=vis,outputvis=outputvis,datacolumn='corrected')


### Image self-cal'd EBs ###

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_selfcal_image'
        delete_tclean_output(imagename)
        tclean_wrapper(vis=prefix+'_'+p['name']+'_initcont_selfcal.ms',
                        imagename=imagename,
                        deconvolver='multiscale',
                        scales=scales[baseline_key],
                        mask=mask,
                        threshold=thresholds[baseline_key],
                        cellsize=cellsize[baseline_key],
                        imsize=imsize[baseline_key],
                        parallel=use_parallel,
                        )
        output_image = f'{imagename}.image'
        estimate_SNR(output_image,disk_mask=mask,noise_mask=noise_annulus)
        rms = p['rms']
        generate_image_png(image=output_image,plot_sizes=image_png_plot_sizes,
                           color_scale_limits=[-3*rms,10*rms],
                           save_folder=individual_EB_selfcal_folder,
                           mask=f'{imagename}.mask',noise_annulus=noise_annulus)
#CQ_Tau_LB_EB0_initcont_selfcal_image.image
#Beam 0.106 arcsec x 0.065 arcsec (-32.80 deg)
#Flux inside disk mask: 437.50 mJy
#Peak intensity of source: 10.90 mJy/beam
#rms: 6.86e-02 mJy/beam
#Peak SNR: 158.87

#CQ_Tau_LB_EB1_initcont_selfcal_image.image
#Beam 0.111 arcsec x 0.070 arcsec (-35.64 deg)
#Flux inside disk mask: 447.67 mJy
#Peak intensity of source: 12.14 mJy/beam
#rms: 8.47e-02 mJy/beam
#Peak SNR: 143.34

#CQ_Tau_LB_EB2_initcont_selfcal_image.image
#Beam 0.102 arcsec x 0.073 arcsec (-30.58 deg)
#Flux inside disk mask: 454.65 mJy
#Peak intensity of source: 11.89 mJy/beam
#rms: 7.29e-02 mJy/beam
#Peak SNR: 163.05

#CQ_Tau_LB_EB3_initcont_selfcal_image.image
#Beam 0.096 arcsec x 0.073 arcsec (-15.57 deg)
#Flux inside disk mask: 443.01 mJy
#Peak intensity of source: 11.35 mJy/beam
#rms: 5.39e-02 mJy/beam
#Peak SNR: 210.35

#CQ_Tau_SB_EB0_initcont_selfcal_image.image
#Beam 0.629 arcsec x 0.483 arcsec (-12.42 deg)
#Flux inside disk mask: 426.60 mJy
#Peak intensity of source: 172.01 mJy/beam
#rms: 5.72e-01 mJy/beam
#Peak SNR: 300.59

#CQ_Tau_SB_EB1_initcont_selfcal_image.image
#Beam 0.629 arcsec x 0.472 arcsec (-14.10 deg)
#Flux inside disk mask: 421.95 mJy
#Peak intensity of source: 156.66 mJy/beam
#rms: 5.62e-01 mJy/beam
#Peak SNR: 278.77



for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_selfcal_image'
        #ratio image, to be compared to the ratio image after alignment
        ref_image = f'{prefix}_{baseline_key}_EB0_initcont_selfcal_image.image'
        ref_rms = params[f'{baseline_key}0']['rms']
        ratio_image = imagename+'.ratio'
        os.system(f'rm -rf {ratio_image}')
        immath(imagename=[ref_image,imagename+'.image'],mode='evalexpr',
               outfile=ratio_image,expr=f'iif(IM0 > 3*{ref_rms}, IM1/IM0, 0)')
        generate_image_png(ratio_image,plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                           color_scale_limits=[0.5,1.5],image_units='ratio',
                           save_folder=individual_EB_selfcal_folder)


######
# ALIGN DATA (go from *initcont_selfcal.ms to *initcont_shift.ms)
######

alignment_folder = get_figures_folderpath('5_alignment_figures')
make_figures_folder(alignment_folder)

# Select the LB EB to act as the reference (usually the best SNR one).

reference_for_LB_alignment = f'{prefix}_LB_EB3_initcont_selfcal.ms'
assert 'LB' in reference_for_LB_alignment,\
            'you need to choose an LB EB for alignment of LB'

alignment_offsets = {}

# All the other EBs will be aligned to the reference EB
# We also include the reference EB itself to make sure the coordinate changes are
# copied over.

offset_LB_EBs = ['{}_{}_initcont_selfcal.ms'.format(prefix, params['name'])
                 for params in data_params_LB.values()]

#select the continuum spw with the large bandwidth
continuum_spw_id = 1
# Find the relative offsets and update the phase centers for all offset_EBs.
# cell_size defines the size of the uv grid
alignment_npix = {'LB':1024,'SB':102}
alignment_cell_size = {'LB':0.01,'SB':0.1}
alignment_plot_file_template = os.path.join(alignment_folder,
                                            'alignment_uv_grid.png')
alignment.align_measurement_sets(reference_ms=reference_for_LB_alignment,
                                 align_ms=offset_LB_EBs,npix=alignment_npix['LB'],
                                 cell_size=alignment_cell_size['LB'],
                                 spwid=continuum_spw_id,plot_uv_grid=True,
                                 plot_file_template=alignment_plot_file_template)
#New coordinates for CQ_Tau_LB_EB0_initcont_selfcal.ms
#requires a shift of [0.0081127,-0.0038208]

#New coordinates for CQ_Tau_LB_EB1_initcont_selfcal.ms
#requires a shift of [0.0085539,-0.0062151]

#New coordinates for CQ_Tau_LB_EB2_initcont_selfcal.ms
#requires a shift of [0.0059263,0.002476]

#New coordinates for CQ_Tau_LB_EB3_initcont_selfcal.ms
#no shift, reference MS.


#insert offsets from the alignment output
alignment_offsets['LB_EB0'] = [0.0081127,-0.0038208]
alignment_offsets['LB_EB1'] = [0.0085539,-0.0062151]
alignment_offsets['LB_EB2'] = [0.0059263,0.002476]
alignment_offsets['LB_EB3'] = [0,0]

shifted_LB_EBs = [EB.replace('.ms','_shift.ms') for EB in offset_LB_EBs]

#to check if alignment worked, calculate shift again and verify that shifts are small (i.e.
#a fraction of the cell size):

for shifted_ms in shifted_LB_EBs:
    if shifted_ms == reference_for_LB_alignment.replace('.ms','_shift.ms'):
        #for some reason the fitter fails when computing the offset of an EB to itself,
        #so we skip the ref EB
        continue
    offset = alignment.find_offset(reference_ms=reference_for_LB_alignment,
                                   offset_ms=shifted_ms,npix=alignment_npix['LB'],
                                   cell_size=alignment_cell_size['LB'],
                                   spwid=continuum_spw_id)
    print(f'#offset for {shifted_ms}: ',offset)

#offset for CQ_Tau_LB_EB0_initcont_selfcal_shift.ms:  [-3.73809410e-05  6.71375289e-05]
#offset for CQ_Tau_LB_EB1_initcont_selfcal_shift.ms:  [4.23395663e-04 7.11468287e-05]
#offset for CQ_Tau_LB_EB2_initcont_selfcal_shift.ms:  [5.16357229e-04 5.13073117e-05]


# Merge shifted LB EBs for aligning SB EBs
LB_concat_shifted = f'{prefix}_LB_concat_shifted.ms'
os.system(f'rm -rf {LB_concat_shifted}')
concat(vis=shifted_LB_EBs,concatvis=LB_concat_shifted,dirtol='0.1arcsec',
       copypointing=False)


# Align SB EBs to concat shifted LB EBs
reference_for_SB_alignment = LB_concat_shifted
offset_SB_EBs = ['{}_{}_initcont_selfcal.ms'.format(prefix, params['name'])
                 for params in data_params_SB.values()]
alignment.align_measurement_sets(reference_ms=reference_for_SB_alignment,
                                 align_ms=offset_SB_EBs,npix=alignment_npix['SB'],
                                 cell_size=alignment_cell_size['SB'],
                                 spwid=continuum_spw_id,plot_uv_grid=True,
                                 plot_file_template=alignment_plot_file_template)
#New coordinates for CQ_Tau_SB_EB0_initcont_selfcal.ms
#requires a shift of [0.045171,-0.060395]

#New coordinates for CQ_Tau_SB_EB1_initcont_selfcal.ms
#requires a shift of [0.07714,-0.10134]


alignment_offsets['SB_EB0'] = [0.045171,-0.060395]
alignment_offsets['SB_EB1'] = [0.07714,-0.10134]

shifted_SB_EBs = [EB.replace('.ms','_shift.ms') for EB in offset_SB_EBs]

#check by calculating offset again
for shifted_ms in shifted_SB_EBs:
    offset = alignment.find_offset(reference_ms=reference_for_SB_alignment,
                                   offset_ms=shifted_ms,npix=alignment_npix['SB'],
                                   cell_size=alignment_cell_size['SB'],
                                   spwid=continuum_spw_id)
    print(f'#offset for {shifted_ms}: ',offset)
#offset for CQ_Tau_SB_EB0_initcont_selfcal_shift.ms:  [-1.28288179e-05 -3.40566829e-04]
#offset for CQ_Tau_SB_EB1_initcont_selfcal_shift.ms:  [ 0.00191349 -0.00076363]

#check also shift of SB EBs to each other after alignment:
for shifted_ms in shifted_SB_EBs[1:]:
    offset = alignment.find_offset(reference_ms=shifted_SB_EBs[0],
                                   offset_ms=shifted_ms,npix=alignment_npix['SB'],
                                   cell_size=alignment_cell_size['SB'],
                                   spwid=continuum_spw_id)
    print(f'#offset for {shifted_ms} to SB EB0: ',offset)
#offset for CQ_Tau_SB_EB1_initcont_selfcal_shift.ms to SB EB0:  [ 0.00143266 -0.00449592]

# Remove the '_selfcal' part of the names to match the naming convention below.
for shifted_EB in shifted_LB_EBs+shifted_SB_EBs:
    os.system('mv {} {}'.format(shifted_EB, shifted_EB.replace('_selfcal', '')))


""" Check that the images are indeed aligned after the shift """

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_shift_image'
        tclean_wrapper(vis=prefix+'_'+p['name']+'_initcont_shift.ms',
                        imagename=imagename,
                        deconvolver='multiscale',
                        scales=scales[baseline_key],
                        mask=mask,
                        threshold=thresholds[baseline_key],
                        cellsize=cellsize[baseline_key],
                        imsize=imsize[baseline_key],
                        parallel=use_parallel,
                      )
        estimate_SNR(f'{imagename}.image',disk_mask=mask,
                     noise_mask=noise_annulus)
        rms = p['rms']
        generate_image_png(f'{imagename}.image',plot_sizes=image_png_plot_sizes,
                           color_scale_limits=[-3*rms,10*rms],
                           save_folder=alignment_folder,
                           mask=f'{imagename}.mask')

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_shift_image'
        ref_image = f'{prefix}_{baseline_key}_EB0_initcont_shift_image.image'
        ref_rms = params[f'{baseline_key}0']['rms']
        ratio_image = imagename+'.ratio'
        os.system(f'rm -rf {ratio_image}')
        immath(imagename=[ref_image,imagename+'.image'],mode='evalexpr',
               outfile=ratio_image,expr=f'iif(IM0 > 3*{ref_rms}, IM1/IM0, 0)')
        generate_image_png(ratio_image,plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                           color_scale_limits=[0.5,1.5],image_units='ratio',
                           save_folder=alignment_folder)


""" Now that everything is aligned, we inspect the flux calibration. """

for params in data_params.values():
    msfile = prefix+'_'+params['name']+'_initcont_shift.ms'
    export_MS(msfile) #export MS contents into Numpy save files

list_npz_files = []
for baseline_key,n_EB in number_of_EBs.items():
    list_npz_files += [f'{prefix}_{baseline_key}_EB{i}_initcont_shift.vis.npz'
                       for i in range(n_EB)]

deprojected_vis_profiles_folder = get_figures_folderpath('6_deprojected_vis_profiles')
make_figures_folder(deprojected_vis_profiles_folder)

""" Plot deprojected visibility profiles for all data together """
plot_deprojected(filelist=list_npz_files,
                 fluxscale=[1.]*(number_of_EBs['LB']+number_of_EBs['SB']),PA=PA,
                 incl=incl,show_err=True,
                 plot_label=os.path.join(deprojected_vis_profiles_folder,
                                         f'{prefix}_flux_scale_EB_preselfcal.png'))

flux_comparison_folder = get_figures_folderpath('7_flux_comparisons')
make_figures_folder(flux_comparison_folder)

#we choose an EB to compare the flux scaling; if possible the best SB EB, but in the
#case of CQ Tau, SB sufferts from decoherence, so we choose an LB EB instead
flux_ref_EB = 'LB_EB3'

for params in data_params.values():
    plot_label = os.path.join(flux_comparison_folder,
                              'flux_comparison_'+params['name']+f'_to_{flux_ref_EB}.png')
    estimate_flux_scale(reference=f'{prefix}_{flux_ref_EB}_initcont_shift.vis.npz',
                        comparison=prefix+'_'+params['name']+'_initcont_shift.vis.npz',
                        incl=incl, PA=PA,plot_label=plot_label)
#The ratio of the fluxes of CQ_Tau_LB_EB0_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 0.96966
#The scaling factor for gencal is 0.985 for your comparison measurement
#The error on the weighted mean ratio is 3.965e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_LB_EB1_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 0.98970
#The scaling factor for gencal is 0.995 for your comparison measurement
#The error on the weighted mean ratio is 3.834e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_LB_EB2_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 1.01936
#The scaling factor for gencal is 1.010 for your comparison measurement
#The error on the weighted mean ratio is 3.665e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_LB_EB3_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 1.00000
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 3.231e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SB_EB0_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 0.91170
#The scaling factor for gencal is 0.955 for your comparison measurement
#The error on the weighted mean ratio is 2.592e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SB_EB1_initcont_shift.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 0.85586
#The scaling factor for gencal is 0.925 for your comparison measurement
#The error on the weighted mean ratio is 3.053e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


#LB values are all within a few % of one another. SB data are off, possibly due
#to the "waterfall features" (decoherence)

"""

SB has overlapping uv ranges with both ACA and LB, so
we select an SB EB as the reference for flux scaling.

In case this source does not have ACA data, simply ignore the ACA steps in the list below.
 
If all SB EBs suffer from decoherence and cannot be used, see below

We correct flux differences >4%, if phase noise looks reasonable

If you need to re-scale fluxes, use command:
rescale_flux(vis=prefix+'_LB_EB0_initcont_shift.ms', gencalparameter=[1.044])

The general procedure is as follows:
1) check flux scaling, and if flux differences are >4%, re-scale the fluxes unless
    there is clear de-coherence (e.g. seen as a systematic decrease of scaling with UV distance)
2) do not scale those EBs with phase decoherence. Proceed with self-cal following the steps below 
   to the point that those EBs are self calibrated
3) check flux scaling again
4) if no flux scaling has been applied in 1), and you now still see a flux offset, then
    apply the flux scaling determined in step 3) to the non-self caled EBs
    and repeat the self-cal

If all SB EBs suffer from phase decoherence and cannot be used as the reference
for flux scaling:
1) concat ACA data and self-cal them in phase
2) concat them to SB data and self-cal them in phase
3) check flux offsets
4) if there are flux offsets, go back to 1), but before re-starting the process apply
    the corrections to the non-self caled ACA and SB data, and re-do steps 1) - 3).
    For this you should add additional code (essentially copy/paste the code from the first
    iteration) that repeats steps 1-3, but saves all output with different filenames. Remember
    to use the right filenames when you continue the script after step 6
5) Check flux offsets of LB EBs to a correct SB EB
6) concat LB data to ACA+SB and continue with script

"""
#For CQ Tau, we see clear decoherence, so we proceed with SB self-cal without flux-scaling

############ START of SB self-cal ############
#for phase cal, clean down to 6 sigma

SB_selfcal_folder = get_figures_folderpath('8_selfcal_SB_figures')
make_figures_folder(SB_selfcal_folder)

SB_cont_p0 = prefix+'_SB_contp0'
os.system('rm -rf %s.ms*' %SB_cont_p0)
concat(vis=[f'{prefix}_SB_EB{i}_initcont_shift.ms' for i in range(number_of_EBs['SB'])],
       concatvis=SB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)

listobs(vis=SB_cont_p0+'.ms',listfile=SB_cont_p0+'.ms.txt',overwrite=True)

"""Define new SB mask using new center read from listobs"""
mask_ra = '05h35m58.4734s'
mask_dec = '+24.44.53.4989'

SB_mask= f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
         +f'{mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'

noise_annulus_SB = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"

SB_tclean_wrapper_kwargs = {'mask':SB_mask,'deconvolver':'multiscale',
                            'scales':scales['SB'],'savemodel':'modelcolumn',
                            'imsize':imsize['SB'],'cellsize':cellsize['SB'],
                            'robust':0.5,'interactive':False,
                            'parallel':use_parallel,'gridder':'standard'}

#go down to ~6 sigma
delete_tclean_output(SB_cont_p0)
tclean_wrapper(vis=SB_cont_p0+'.ms',imagename = SB_cont_p0,
               threshold = f'{6*0.35}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p0+'.image', disk_mask = SB_mask,noise_mask = noise_annulus_SB)
#CQ_Tau_SB_contp0.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 427.18 mJy
#Peak intensity of source: 168.26 mJy/beam
#rms: 3.53e-01 mJy/beam
#Peak SNR: 476.31

rms_SB = imstat(imagename = SB_cont_p0+'.image',region = noise_annulus_SB)['rms'][0]
generate_image_png(SB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder,mask=SB_cont_p0+'.mask',
                   noise_annulus=noise_annulus_SB)

""" Look for references antennas from weblog, and pick the first that are listed, overlapping with all EBs """
# both EBs: DV08, DA57, DA52, DV14

""" Get station numbers """
for ref_ant in ('DV08','DA57'):
    get_station_numbers(SB_cont_p0+'.ms',ref_ant)
#Observation ID 0: DV08@A036
#Observation ID 1: DV08@A036
#Observation ID 0: DA57@A001
#Observation ID 1: DA57@A001


SB_contspws = '0~7'
SB_refant   = 'DV08@A036, DA57@A001'
SB_spw_mapping = [0,0,0,0,4,4,4,4]

#First round
SB_p1 = prefix+'_SB.p1'
os.system('rm -rf '+SB_p1)
gaincal(vis=SB_cont_p0+'.ms',caltable=SB_p1,
        gaintype='G', spw=SB_contspws,refant=SB_refant, combine='scan,spw',
        calmode='p', solint='inf', minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,
                             f'{prefix}_SB_gain_p1_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p0+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping,gaintable=[SB_p1], interp='linearPD',
         calwt=True, applymode='calonly')

SB_cont_p1 = SB_cont_p0.replace('p0','p1')
os.system('rm -rf %s.ms*' % SB_cont_p1)
split(vis=SB_cont_p0+'.ms',outputvis=SB_cont_p1+'.ms', datacolumn='corrected')

#again threshold should be ~6sigma
delete_tclean_output(SB_cont_p1)
tclean_wrapper(vis=SB_cont_p1+'.ms',imagename = SB_cont_p1,
               threshold = f'{6*0.35}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p1+'.image',disk_mask=SB_mask,noise_mask=noise_annulus_SB)
#CQ_Tau_SB_contp1.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.96 deg)
#Flux inside disk mask: 427.12 mJy
#Peak intensity of source: 168.03 mJy/beam
#rms: 3.47e-01 mJy/beam
#Peak SNR: 484.93

generate_image_png(SB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)


#Second round
SB_p2 = SB_p1.replace('p1','p2')
os.system('rm -rf '+SB_p2)
gaincal(vis=SB_cont_p1+'.ms',caltable=SB_p2,
        gaintype='T',spw=SB_contspws,refant=SB_refant, combine='scan, spw',
        calmode='p', solint='360s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_gain_p2_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=SB_cont_p1+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping, gaintable=[SB_p2], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p2 = SB_cont_p1.replace('p1','p2')
os.system('rm -rf %s.ms*' % SB_cont_p2)
split(vis=SB_cont_p1+'.ms',outputvis=SB_cont_p2+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p2)
tclean_wrapper(vis=SB_cont_p2+'.ms',imagename = SB_cont_p2,
               threshold = f'{6*0.18}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p2+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_SB_contp2.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.96 deg)
#Flux inside disk mask: 428.85 mJy
#Peak intensity of source: 175.47 mJy/beam
#rms: 1.74e-01 mJy/beam
#Peak SNR: 1008.67

generate_image_png(SB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)


#Third round
SB_p3 = SB_p2.replace('p2','p3')
os.system('rm -rf '+SB_p3)
gaincal(vis=SB_cont_p2+'.ms', caltable=SB_p3,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw',
        calmode='p', solint='120s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_gain_p3_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p2+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p3], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p3 = SB_cont_p2.replace('p2','p3')
os.system('rm -rf %s.ms*' % SB_cont_p3)
split(vis=SB_cont_p2+'.ms',outputvis=SB_cont_p3+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p3)
tclean_wrapper(vis=SB_cont_p3+'.ms',imagename = SB_cont_p3,
               threshold = f'{6*0.15}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p3+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_SB_contp3.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.96 deg)
#Flux inside disk mask: 429.48 mJy
#Peak intensity of source: 178.63 mJy/beam
#rms: 1.47e-01 mJy/beam
#Peak SNR: 1216.01

generate_image_png(SB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)

#Fourth round
SB_p4 = SB_p3.replace('p3','p4')
os.system('rm -rf '+SB_p4)
gaincal(vis=SB_cont_p3+'.ms', caltable=SB_p4,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='60s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_gain_p4_phase_vs_time.png')
       )

applycal(vis=SB_cont_p3+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p4], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p4 = SB_cont_p3.replace('p3','p4')
os.system('rm -rf %s.ms*' % SB_cont_p4)
split(vis=SB_cont_p3+'.ms',outputvis=SB_cont_p4+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p4)
tclean_wrapper(vis=SB_cont_p4+'.ms',imagename = SB_cont_p4,
               threshold = f'{6*0.15}mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p4+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_SB_contp4.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.96 deg)
#Flux inside disk mask: 429.81 mJy
#Peak intensity of source: 180.74 mJy/beam
#rms: 1.50e-01 mJy/beam
#Peak SNR: 1203.94

generate_image_png(SB_cont_p4+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)


#Fifth round
SB_p5 = SB_p4.replace('p4','p5')
os.system('rm -rf '+SB_p5)
gaincal(vis=SB_cont_p4+'.ms', caltable=SB_p5,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='20s', minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_gain_p5_phase_vs_time.png')
       )

applycal(vis=SB_cont_p4+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p5], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p5 = SB_cont_p4.replace('p4','p5')
os.system('rm -rf %s.ms*' % SB_cont_p5)
split(vis=SB_cont_p4+'.ms',outputvis=SB_cont_p5+'.ms',datacolumn='corrected')


""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p5)
tclean_wrapper(vis=SB_cont_p5+'.ms',imagename = SB_cont_p5,
               threshold = f'{6*0.15}mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p5+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_SB_contp5.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.96 deg)
#Flux inside disk mask: 431.00 mJy
#Peak intensity of source: 185.68 mJy/beam
#rms: 1.48e-01 mJy/beam
#Peak SNR: 1252.49

generate_image_png(SB_cont_p5+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)


#Check how SB selfcal improved things
#we do the check for each step of the self-cal to see how things improve at each step
non_self_caled_SB_vis = SB_cont_p0
self_caled_SB_visibilities = {'p1':SB_cont_p1,
                              'p2':SB_cont_p2,
                              'p3':SB_cont_p3,
                              'p4':SB_cont_p4,
                              'p5':SB_cont_p5}
    
SB_EBs = ('EB0','EB1')
SB_EB_spws = ('0,1,2,3','4,5,6,7')

for self_cal_step,self_caled_vis in self_caled_SB_visibilities.items():
    for EB_key,spw in zip(SB_EBs,SB_EB_spws):
        nametemplate = f'{prefix}_SB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',non_self_caled_SB_vis+'.ms']
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['SB'],output_folder=SB_selfcal_folder)
#we can see that self-cal corrects the waterfall features

all_SB_visibilities = self_caled_SB_visibilities.copy()
all_SB_visibilities['p0'] = SB_cont_p0

for self_cal_step,vis_name in all_SB_visibilities.items():
    #split out SB EBs
    vis_ms = vis_name+'.ms'
    nametemplate = vis_ms.replace('.ms','_EB')
    split_all_obs(msfile=vis_ms,nametemplate=nametemplate)
    exported_ms = []
    for i in range(number_of_EBs['SB']):
        EB_vis = f'{nametemplate}{i}.ms'
        export_MS(EB_vis) #export MS contents into Numpy save files
        exported_ms.append(EB_vis.replace('.ms','.vis.npz'))
    for i,exp_ms in enumerate(exported_ms):
        png_filename = f'flux_comparison_SB_EB{i}_{self_cal_step}_to_{flux_ref_EB}.png'
        plot_label = os.path.join(SB_selfcal_folder,png_filename)
        estimate_flux_scale(reference=f'{prefix}_{flux_ref_EB}_initcont_shift.vis.npz',
                            comparison=exp_ms,incl=incl,PA=PA,plot_label=plot_label)
    fluxscale = [1.,]*number_of_EBs['SB']
    plot_label = os.path.join(SB_selfcal_folder,
                              f'deprojected_vis_profiles_SB_{self_cal_step}.png')
    plot_deprojected(filelist=exported_ms,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)

#The ratio of the fluxes of CQ_Tau_SB_contp5_EB0.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 1.00164
#The scaling factor for gencal is 1.001 for your comparison measurement
#The error on the weighted mean ratio is 2.765e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SB_contp5_EB1.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 0.96264
#The scaling factor for gencal is 0.981 for your comparison measurement
#The error on the weighted mean ratio is 3.222e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


#self-cal removed the decoherence
#Now we do LB self-cal

############ END of SB self-cal ############

"""SELF-CAL COMBINED DATA"""
#phase cal down to 6 sigma, amp cal down to 1 sigma

LB_selfcal_folder = get_figures_folderpath('9_selfcal_SBLB_figures')
make_figures_folder(LB_selfcal_folder)

""" Merge the SB self-cal'ed ms with LB ms"""
LB_cont_p0 = prefix+'_SBLB_contp0'
os.system('rm -rf %s.ms*' % LB_cont_p0)
concat(vis=[SB_cont_p5+'.ms']+[f'{prefix}_LB_EB{i}_initcont_shift.ms' for i
                               in range(number_of_EBs['LB'])],
            concatvis=LB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)

listobs(vis=LB_cont_p0+'.ms',listfile=LB_cont_p0+'.ms.txt',overwrite=True)

mask_ra = '05h35m58.4734s'
mask_dec = '24.44.53.4989'

LB_mask = f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
         +f' {mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'
noise_annulus_LB = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"

LB_tclean_wrapper_kwargs = {'mask':LB_mask,'deconvolver':'multiscale',
                            'scales':scales['LB'],'savemodel':'modelcolumn',
                            'imsize':imsize['LB'],'cellsize':cellsize['LB'],
                            'robust':0.5,'interactive':False,
                            'parallel':use_parallel,'gridder':'standard'}


""" clean down to ~6 sigma"""
delete_tclean_output(LB_cont_p0)
tclean_wrapper(vis=LB_cont_p0+'.ms', imagename = LB_cont_p0,
               threshold = f'{6*0.04}mJy',**LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p0+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
#CQ_Tau_SBLB_contp0.image
#Beam 0.111 arcsec x 0.078 arcsec (-29.49 deg)
#Flux inside disk mask: 431.51 mJy
#Peak intensity of source: 13.46 mJy/beam
#rms: 3.32e-02 mJy/beam
#Peak SNR: 405.46

rms_LB = imstat(imagename = LB_cont_p0+'.image', region = noise_annulus_LB)['rms'][0]
generate_image_png(LB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder,mask=LB_cont_p0+'.mask',
                   noise_annulus=noise_annulus_LB)


""" Look for references antennas from weblog, and pick the first that are listed, overlapping with all EBs """
""" Since we are combined SB EBs to LB EBs, ref antennas for both SB and LB are needed """
# For LB:
# EB0: DV03, DA60, DA65, DV04, DV21, DV20
# EB1: DA43, DV04, DA60, DV25, DV21, DV03
# EB2: DA43, DA60, DV03, DV04, DV25, DV21
# EB3: DA43, DV04, DV25, DV21, DV03, DA62

# For SB, we used: DV08@A036, DA57@A001
for ref_ant in ('DA43','DV03','DA60', 'DV08','DA57'):
    get_station_numbers(LB_cont_p0+'.ms',ref_ant)
#Observation ID 1: DA43@A035
#Observation ID 2: DA43@A035
#Observation ID 3: DA43@A035
#Observation ID 4: DA43@A073
#Observation ID 5: DA43@A073
#Observation ID 0: DV03@A027
#Observation ID 1: DV03@A027
#Observation ID 2: DV03@A027
#Observation ID 3: DV03@A027
#Observation ID 4: DV03@A027
#Observation ID 5: DV03@A027
#Observation ID 0: DA60@A042
#Observation ID 1: DA60@A042
#Observation ID 2: DA60@A042
#Observation ID 3: DA60@A042
#Observation ID 4: DA60@A042
#Observation ID 5: DA60@A042
#Observation ID 0: DV08@A101
#Observation ID 1: DV08@A101
#Observation ID 2: DV08@A101
#Observation ID 3: DV08@A101
#Observation ID 4: DV08@A036
#Observation ID 5: DV08@A036
#Observation ID 0: DA57@A124
#Observation ID 1: DA57@A124
#Observation ID 2: DA57@A124
#Observation ID 3: DA57@A124
#Observation ID 4: DA57@A001
#Observation ID 5: DA57@A001

# We use DV08@A036, DA57@A001 for the SBs again, no overlap with LB EBs
# We use DA43@A035 for LB EB1,2,3 (antenna not present in LB EB0, and no overlap
# with SB EBs), and DV03@A027 for LB EB0

""" Self-calibration parameters """
LB_contspws = '0~23'
LB_refant   = 'DV08@A036, DA57@A001, DA43@A035, DV03@A027'

LB_spw_mapping = [0,0,0,0,4,4,4,4,8,8,8,8,12,12,12,12,16,16,16,16,20,20,20,20]

LB_p1 = prefix+'_SBLB.p1'
os.system('rm -rf '+LB_p1)
#if you get many flagged solutions, change gaintype to 'T'
gaincal(vis=LB_cont_p0+'.ms', caltable=LB_p1, gaintype='G', spw=LB_contspws,
        refant=LB_refant, combine='scan,spw', calmode='p', solint='inf',
        minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_gain_p1_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p0+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p1], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p1 = prefix+'_SBLB_contp1'
os.system('rm -rf %s.ms*' % LB_cont_p1)
split(vis=LB_cont_p0+'.ms', outputvis=LB_cont_p1+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(LB_cont_p1)
tclean_wrapper(vis=LB_cont_p1+'.ms',imagename=LB_cont_p1,threshold=f'{6*0.033}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p1+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
#CQ_Tau_SBLB_contp1.image
#Beam 0.111 arcsec x 0.078 arcsec (-29.49 deg)
#Flux inside disk mask: 433.02 mJy
#Peak intensity of source: 13.41 mJy/beam
#rms: 3.21e-02 mJy/beam
#Peak SNR: 417.81

generate_image_png(LB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)


""" Second round of phase-only self-cal """
LB_p2 = prefix+'_SBLB.p2'
os.system('rm -rf '+LB_p2)
#solint='360s' resulted in "mismatched frequencies" error, so I slighlty changed it
gaincal(vis=LB_cont_p1+'.ms', caltable=LB_p2, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='p', solint='370s',
        minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_gain_p2_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p1+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p2], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p2 = prefix+'_SBLB_contp2'
os.system('rm -rf %s.ms*' % LB_cont_p2)
split(vis=LB_cont_p1+'.ms', outputvis=LB_cont_p2+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(LB_cont_p2)
tclean_wrapper(vis=LB_cont_p2+'.ms', imagename = LB_cont_p2, threshold = f'{6*0.03}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p2+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_SBLB_contp2.image
#Beam 0.111 arcsec x 0.078 arcsec (-29.49 deg)
#Flux inside disk mask: 433.28 mJy
#Peak intensity of source: 13.53 mJy/beam
#rms: 2.92e-02 mJy/beam
#Peak SNR: 462.55

generate_image_png(LB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)


""" Third round of phase-only self-cal """
"""
Check scan length to decide whether to combine scans. If scans are 2 min,
do not combine them. If they are shorter, combine scans here
"""
#LB scans are ~1min
LB_p3 = prefix+'_SBLB.p3'
os.system('rm -rf '+LB_p3)
gaincal(vis=LB_cont_p2+'.ms', caltable=LB_p3, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='p', solint='120s',
        minsnr=2., minblperant=4)
#quite some flagging, up to 13/46 solutions flagged

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_gain_p3_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p2+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p3], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p3 = prefix+'_SBLB_contp3'
os.system('rm -rf %s.ms*' % LB_cont_p3)
split(vis=LB_cont_p2+'.ms', outputvis=LB_cont_p3+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = f'{6*0.029}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_SBLB_contp3.image
#Beam 0.111 arcsec x 0.078 arcsec (-29.49 deg)
#Flux inside disk mask: 433.44 mJy
#Peak intensity of source: 13.82 mJy/beam
#rms: 2.80e-02 mJy/beam
#Peak SNR: 492.96

generate_image_png(LB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)



#Now check the flux scaling

self_caled_LB_visibilities = {'p1':LB_cont_p1,
                              'p2':LB_cont_p2,
                              'p3':LB_cont_p3}

for vis in self_caled_LB_visibilities.values(): 
    listobs(vis=vis+'.ms',listfile=vis+'.ms.txt',overwrite=True)

LB_EBs = ('EB0','EB1','EB2','EB3')
LB_EB_spws = ('0,1,2,3','4,5,6,7','8,9,10,11','12,13,14,15') #fill out by referring to listobs output

for self_cal_step,self_caled_vis in self_caled_LB_visibilities.items():
    for EB_key,spw in zip(LB_EBs,LB_EB_spws):
        nametemplate = f'{prefix}_LB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',LB_cont_p0+'.ms']
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['LB'],output_folder=LB_selfcal_folder)

#set to the EB of the combined SBLB data that corresponds to flux_ref_EB
SBLB_flux_ref_EB = 3 #this is LB EB3

total_number_of_EBs = number_of_EBs['SB'] + number_of_EBs['LB']
for self_cal_step,vis_name in self_caled_LB_visibilities.items():
    vis_ms = vis_name+'.ms'
    nametemplate = vis_ms.replace('.ms','_EB')
    split_all_obs(msfile=vis_ms,nametemplate=nametemplate)
    for i in range(total_number_of_EBs):
        export_MS(f'{nametemplate}{i}.ms')
    for i in range(total_number_of_EBs):
        reference = f'{nametemplate}{SBLB_flux_ref_EB}.vis.npz'
        output = f'flux_comparison_EB{i}_to_EB{SBLB_flux_ref_EB}'\
                       +f'_SBLB_{self_cal_step}.png'
        plot_label = os.path.join(LB_selfcal_folder,output)
        estimate_flux_scale(reference=reference,
                            comparison=f'{nametemplate}{i}.vis.npz',
                            incl=incl, PA=PA,uvbins = np.arange(40.,300.,20.),
                            plot_label=plot_label)
    filelist = [f'{nametemplate}{i}.vis.npz' for i in range(total_number_of_EBs)]
    fluxscale = [1.,]*total_number_of_EBs
    plot_label = os.path.join(LB_selfcal_folder,
                              f'deprojected_vis_profiles_SBLB_{self_cal_step}.png')
    plot_deprojected(filelist=filelist,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)

#flux offsets after phase self-cal:

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB0.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 0.96455
#The scaling factor for gencal is 0.982 for your comparison measurement
#The error on the weighted mean ratio is 4.227e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB1.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 1.01701
#The scaling factor for gencal is 1.008 for your comparison measurement
#The error on the weighted mean ratio is 4.133e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB2.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 1.03792
#The scaling factor for gencal is 1.019 for your comparison measurement
#The error on the weighted mean ratio is 3.957e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB3.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 1.00000
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 3.496e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB4.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 0.99598
#The scaling factor for gencal is 0.998 for your comparison measurement
#The error on the weighted mean ratio is 2.805e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_SBLB_contp3_EB5.vis.npz to
#CQ_Tau_SBLB_contp3_EB3.vis.npz is 0.95629
#The scaling factor for gencal is 0.978 for your comparison measurement
#The error on the weighted mean ratio is 3.254e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


#self-cal clearly improved decoherences

#however, there is still an offset larger than 4% aftter phase self-cal, we scale and
#repeat the self-cal

data_params['SB0']['flux_scaling_factor'] = None
data_params['SB1']['flux_scaling_factor'] = 0.978
for i in range(number_of_EBs['LB']):
    data_params[f'LB{i}']['flux_scaling_factor'] = None

for baseline in ('SB','LB'):
    for i in range(number_of_EBs[baseline]):
        scaling_factor = data_params[f'{baseline}{i}']['flux_scaling_factor']
        if scaling_factor is None:
            print(f'copyting {baseline} EB{i}')
            shutil.copytree(src=f'{prefix}_{baseline}_EB{i}_initcont_shift.ms',
                            dst=f'{prefix}_{baseline}_EB{i}_initcont_shift_rescaled.ms')
        else:
            print(f'going to rescale {baseline} EB{i}')
            rescale_flux(vis=f'{prefix}_{baseline}_EB{i}_initcont_shift.ms',
                         gencalparameter=[scaling_factor])


############ BEGIN of SB self-cal iteration 2 ############
SB_scaled_selfcal_folder = get_figures_folderpath('10_scaled_selfcal_SB_figures')
make_figures_folder(SB_scaled_selfcal_folder)

SB_cont_p0 = prefix+'_scaled_SB_contp0'
os.system('rm -rf %s.ms*' %SB_cont_p0)
concat(vis=[f'{prefix}_SB_EB{i}_initcont_shift_rescaled.ms' for i in
            range(number_of_EBs['SB'])],
       concatvis=SB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)

listobs(vis=SB_cont_p0+'.ms',listfile=SB_cont_p0+'.ms.txt',overwrite=True)


#go down to ~6 sigma
delete_tclean_output(SB_cont_p0)
tclean_wrapper(vis=SB_cont_p0+'.ms',imagename = SB_cont_p0,
               threshold = f'{6*0.35}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p0+'.image', disk_mask = SB_mask,noise_mask = noise_annulus_SB)
#CQ_Tau_scaled_SB_contp0.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 431.42 mJy
#Peak intensity of source: 169.93 mJy/beam
#rms: 3.54e-01 mJy/beam
#Peak SNR: 479.53

rms_SB = imstat(imagename = SB_cont_p0+'.image',region = noise_annulus_SB)['rms'][0]
generate_image_png(SB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder,mask=SB_cont_p0+'.mask',
                   noise_annulus=noise_annulus_SB)

#First round
SB_p1 = prefix+'_scaled_SB.p1'
os.system('rm -rf '+SB_p1)
gaincal(vis=SB_cont_p0+'.ms',caltable=SB_p1,
        gaintype='G', spw=SB_contspws,refant=SB_refant, combine='scan,spw',
        calmode='p', solint='inf', minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_scaled_selfcal_folder,
                             f'{prefix}_SB_gain_p1_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p0+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping,gaintable=[SB_p1], interp='linearPD',
         calwt=True, applymode='calonly')

SB_cont_p1 = SB_cont_p0.replace('p0','p1')
os.system('rm -rf %s.ms*' % SB_cont_p1)
split(vis=SB_cont_p0+'.ms',outputvis=SB_cont_p1+'.ms', datacolumn='corrected')

#again threshold should be ~6sigma
delete_tclean_output(SB_cont_p1)
tclean_wrapper(vis=SB_cont_p1+'.ms',imagename = SB_cont_p1,
               threshold = f'{6*0.35}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p1+'.image',disk_mask=SB_mask,noise_mask=noise_annulus_SB)
#CQ_Tau_scaled_SB_contp1.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 431.40 mJy
#Peak intensity of source: 169.98 mJy/beam
#rms: 3.49e-01 mJy/beam
#Peak SNR: 487.36

generate_image_png(SB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder)


#Second round
SB_p2 = SB_p1.replace('p1','p2')
os.system('rm -rf '+SB_p2)
gaincal(vis=SB_cont_p1+'.ms',caltable=SB_p2,
        gaintype='T',spw=SB_contspws,refant=SB_refant, combine='scan, spw',
        calmode='p', solint='360s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_scaled_selfcal_folder,f'{prefix}_SB_gain_p2_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=SB_cont_p1+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping, gaintable=[SB_p2], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p2 = SB_cont_p1.replace('p1','p2')
os.system('rm -rf %s.ms*' % SB_cont_p2)
split(vis=SB_cont_p1+'.ms',outputvis=SB_cont_p2+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p2)
tclean_wrapper(vis=SB_cont_p2+'.ms',imagename = SB_cont_p2,
               threshold = f'{6*0.18}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p2+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_scaled_SB_contp2.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 433.03 mJy
#Peak intensity of source: 177.57 mJy/beam
#rms: 1.69e-01 mJy/beam
#Peak SNR: 1053.37

generate_image_png(SB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder)


#Third round
SB_p3 = SB_p2.replace('p2','p3')
os.system('rm -rf '+SB_p3)
gaincal(vis=SB_cont_p2+'.ms', caltable=SB_p3,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw',
        calmode='p', solint='120s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_scaled_selfcal_folder,f'{prefix}_SB_gain_p3_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p2+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p3], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p3 = SB_cont_p2.replace('p2','p3')
os.system('rm -rf %s.ms*' % SB_cont_p3)
split(vis=SB_cont_p2+'.ms',outputvis=SB_cont_p3+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p3)
tclean_wrapper(vis=SB_cont_p3+'.ms',imagename = SB_cont_p3,
               threshold = f'{6*0.15}mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p3+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_scaled_SB_contp3.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 433.75 mJy
#Peak intensity of source: 180.69 mJy/beam
#rms: 1.49e-01 mJy/beam
#Peak SNR: 1211.43

generate_image_png(SB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder)

#Fourth round
SB_p4 = SB_p3.replace('p3','p4')
os.system('rm -rf '+SB_p4)
gaincal(vis=SB_cont_p3+'.ms', caltable=SB_p4,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='60s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_scaled_selfcal_folder,f'{prefix}_SB_gain_p4_phase_vs_time.png')
       )

applycal(vis=SB_cont_p3+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p4], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p4 = SB_cont_p3.replace('p3','p4')
os.system('rm -rf %s.ms*' % SB_cont_p4)
split(vis=SB_cont_p3+'.ms',outputvis=SB_cont_p4+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p4)
tclean_wrapper(vis=SB_cont_p4+'.ms',imagename = SB_cont_p4,
               threshold = f'{6*0.15}mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p4+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_scaled_SB_contp4.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 434.12 mJy
#Peak intensity of source: 182.76 mJy/beam
#rms: 1.49e-01 mJy/beam
#Peak SNR: 1224.29

generate_image_png(SB_cont_p4+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder)


#Fifth round
SB_p5 = SB_p4.replace('p4','p5')
os.system('rm -rf '+SB_p5)
gaincal(vis=SB_cont_p4+'.ms', caltable=SB_p5,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='20s', minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_scaled_selfcal_folder,f'{prefix}_SB_gain_p5_phase_vs_time.png')
       )

applycal(vis=SB_cont_p4+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p5], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p5 = SB_cont_p4.replace('p4','p5')
os.system('rm -rf %s.ms*' % SB_cont_p5)
split(vis=SB_cont_p4+'.ms',outputvis=SB_cont_p5+'.ms',datacolumn='corrected')


""" Image the results; check the resulting map """
delete_tclean_output(SB_cont_p5)
tclean_wrapper(vis=SB_cont_p5+'.ms',imagename = SB_cont_p5,
               threshold = f'{6*0.15}mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p5+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
#CQ_Tau_scaled_SB_contp5.image
#Beam 0.628 arcsec x 0.480 arcsec (-12.93 deg)
#Flux inside disk mask: 435.29 mJy
#Peak intensity of source: 187.76 mJy/beam
#rms: 1.50e-01 mJy/beam
#Peak SNR: 1254.62

generate_image_png(SB_cont_p5+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_scaled_selfcal_folder)


#Check how SB selfcal improved things
#we do the check for each step of the self-cal to see how things improve at each step
non_self_caled_SB_vis = SB_cont_p0
self_caled_SB_visibilities = {'p1':SB_cont_p1,
                              'p2':SB_cont_p2,
                              'p3':SB_cont_p3,
                              'p4':SB_cont_p4,
                              'p5':SB_cont_p5}
    
SB_EBs = ('EB0','EB1')
SB_EB_spws = ('0,1,2,3','4,5,6,7')

for self_cal_step,self_caled_vis in self_caled_SB_visibilities.items():
    for EB_key,spw in zip(SB_EBs,SB_EB_spws):
        nametemplate = f'{prefix}_SB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',non_self_caled_SB_vis+'.ms']
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['SB'],output_folder=SB_scaled_selfcal_folder)
#we can see that self-cal corrects the waterfall features

all_SB_visibilities = self_caled_SB_visibilities.copy()
all_SB_visibilities['p0'] = SB_cont_p0

for self_cal_step,vis_name in all_SB_visibilities.items():
    #split out SB EBs
    vis_ms = vis_name+'.ms'
    nametemplate = vis_ms.replace('.ms','_EB')
    split_all_obs(msfile=vis_ms,nametemplate=nametemplate)
    exported_ms = []
    for i in range(number_of_EBs['SB']):
        EB_vis = f'{nametemplate}{i}.ms'
        export_MS(EB_vis) #export MS contents into Numpy save files
        exported_ms.append(EB_vis.replace('.ms','.vis.npz'))
    for i,exp_ms in enumerate(exported_ms):
        png_filename = f'flux_comparison_SB_EB{i}_{self_cal_step}_to_{flux_ref_EB}.png'
        plot_label = os.path.join(SB_scaled_selfcal_folder,png_filename)
        estimate_flux_scale(reference=f'{prefix}_{flux_ref_EB}_initcont_shift.vis.npz',
                            comparison=exp_ms,incl=incl,PA=PA,plot_label=plot_label)
    fluxscale = [1.,]*number_of_EBs['SB']
    plot_label = os.path.join(SB_scaled_selfcal_folder,
                              f'deprojected_vis_profiles_SB_{self_cal_step}.png')
    plot_deprojected(filelist=exported_ms,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)
#The ratio of the fluxes of CQ_Tau_scaled_SB_contp5_EB0.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 1.00158
#The scaling factor for gencal is 1.001 for your comparison measurement
#The error on the weighted mean ratio is 2.765e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SB_contp5_EB1.vis.npz to
#CQ_Tau_LB_EB3_initcont_shift.vis.npz is 1.00592
#The scaling factor for gencal is 1.003 for your comparison measurement
#The error on the weighted mean ratio is 3.368e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


#self-cal removed the decoherence
#Now we do LB self-cal

############ END of SB self-cal ############

"""SELF-CAL COMBINED DATA"""
#phase cal down to 6 sigma, amp cal down to 1 sigma

LB_scaled_selfcal_folder = get_figures_folderpath('11_scaled_selfcal_SBLB_figures')
make_figures_folder(LB_scaled_selfcal_folder)

""" Merge the SB self-cal'ed ms with LB ms"""
LB_cont_p0 = prefix+'_scaled_SBLB_contp0'
os.system('rm -rf %s.ms*' % LB_cont_p0)
concat(vis=[SB_cont_p5+'.ms']+[f'{prefix}_LB_EB{i}_initcont_shift_rescaled.ms' for i
                               in range(number_of_EBs['LB'])],
            concatvis=LB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)

listobs(vis=LB_cont_p0+'.ms',listfile=LB_cont_p0+'.ms.txt',overwrite=True)


""" clean down to ~6 sigma"""
delete_tclean_output(LB_cont_p0)
tclean_wrapper(vis=LB_cont_p0+'.ms', imagename = LB_cont_p0,
               threshold = f'{6*0.034}mJy',**LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p0+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contp0.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.48 deg)
#Flux inside disk mask: 436.40 mJy
#Peak intensity of source: 13.45 mJy/beam
#rms: 3.29e-02 mJy/beam
#Peak SNR: 408.31


rms_LB = imstat(imagename = LB_cont_p0+'.image', region = noise_annulus_LB)['rms'][0]
generate_image_png(LB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder,mask=LB_cont_p0+'.mask',
                   noise_annulus=noise_annulus_LB)


LB_p1 = prefix+'_scaled_SBLB.p1'
os.system('rm -rf '+LB_p1)
#if you get many flagged solutions, change gaintype to 'T'
gaincal(vis=LB_cont_p0+'.ms', caltable=LB_p1, gaintype='G', spw=LB_contspws,
        refant=LB_refant, combine='scan,spw', calmode='p', solint='inf',
        minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_p1_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p0+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p1], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p1 = LB_cont_p0.replace('p0','p1')
os.system('rm -rf %s.ms*' % LB_cont_p1)
split(vis=LB_cont_p0+'.ms', outputvis=LB_cont_p1+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(LB_cont_p1)
tclean_wrapper(vis=LB_cont_p1+'.ms',imagename=LB_cont_p1,threshold=f'{6*0.033}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p1+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contp1.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.48 deg)
#Flux inside disk mask: 436.74 mJy
#Peak intensity of source: 13.40 mJy/beam
#rms: 3.20e-02 mJy/beam
#Peak SNR: 418.55

generate_image_png(LB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder)


""" Second round of phase-only self-cal """
LB_p2 = LB_p1.replace('p1','p2')
os.system('rm -rf '+LB_p2)
#solint='360s' resulted in "mismatched frequencies" error, so I slighlty changed it
gaincal(vis=LB_cont_p1+'.ms', caltable=LB_p2, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='p', solint='370s',
        minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_p2_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p1+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p2], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p2 = LB_cont_p1.replace('p1','p2')
os.system('rm -rf %s.ms*' % LB_cont_p2)
split(vis=LB_cont_p1+'.ms', outputvis=LB_cont_p2+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(LB_cont_p2)
tclean_wrapper(vis=LB_cont_p2+'.ms', imagename = LB_cont_p2, threshold = f'{6*0.03}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p2+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contp2.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.48 deg)
#Flux inside disk mask: 437.60 mJy
#Peak intensity of source: 13.54 mJy/beam
#rms: 2.91e-02 mJy/beam
#Peak SNR: 464.90

generate_image_png(LB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder)


""" Third round of phase-only self-cal """
"""
Check scan length to decide whether to combine scans. If scans are 2 min,
do not combine them. If they are shorter, combine scans here
"""
#LB scans are ~1min
LB_p3 = LB_p2.replace('p2','p3')
os.system('rm -rf '+LB_p3)
gaincal(vis=LB_cont_p2+'.ms', caltable=LB_p3, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='p', solint='120s',
        minsnr=2., minblperant=4)
#quite some flagging, up to 13/46 solutions flagged

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_p3_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p2+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p3], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p3 = LB_cont_p2.replace('p2','p3')
os.system('rm -rf %s.ms*' % LB_cont_p3)
split(vis=LB_cont_p2+'.ms', outputvis=LB_cont_p3+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
delete_tclean_output(LB_cont_p3)
tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = f'{6*0.029}mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contp3.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.48 deg)
#Flux inside disk mask: 437.10 mJy
#Peak intensity of source: 13.80 mJy/beam
#rms: 2.78e-02 mJy/beam
#Peak SNR: 496.50

generate_image_png(LB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder)

""" Clean down to 1 sigma before amplitude self-cal"""

tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = '0.028mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contp3.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.48 deg)
#Flux inside disk mask: 437.16 mJy
#Peak intensity of source: 13.83 mJy/beam
#rms: 2.71e-02 mJy/beam
#Peak SNR: 510.37


""" Amplitude self-cal"""
LB_ap0 = LB_p3.replace('p3','ap0')
os.system('rm -rf '+LB_ap0)
gaincal(vis=LB_cont_p3+'.ms', caltable=LB_ap0, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw, scan', calmode='ap', solint='inf', minsnr=5.0,
        minblperant=4, solnorm=False)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_ap0,xaxis='time', yaxis='GainPhase',iteraxis='spw')
plotms(LB_ap0,xaxis='time', yaxis='GainAmp',iteraxis='spw')

""" Print calibration png file """
plotms(LB_ap0,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_ap0_phase_vs_time.png'))
plotms(LB_ap0,xaxis='time', yaxis='GainAmp',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_ap0_amp_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p3+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_ap0], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_ap0 = LB_cont_p3.replace('p3','ap0')
os.system('rm -rf %s.ms*' % LB_cont_ap0)
split(vis=LB_cont_p3+'.ms', outputvis=LB_cont_ap0+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
#clean again down to 1 sigma
tclean_wrapper(vis=LB_cont_ap0+'.ms', imagename = LB_cont_ap0, threshold = '0.028mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_ap0+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#CQ_Tau_scaled_SBLB_contap0.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.10 deg)
#Flux inside disk mask: 438.26 mJy
#Peak intensity of source: 13.83 mJy/beam
#rms: 2.63e-02 mJy/beam
#Peak SNR: 526.17

generate_image_png(LB_cont_ap0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder)


"""ampl self-cal on scan length gave low SNR. Try ampl self-cal on 370s timescale"""
LB_ap1 = LB_ap0.replace('ap0','ap1')
os.system('rm -rf '+LB_ap1)
gaincal(vis=LB_cont_ap0+'.ms', caltable=LB_ap1, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='ap', solint='370s', minsnr=5.0,
        minblperant=4, solnorm=False)
##about 10% flagged, but occasionally higher 

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_ap1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
plotms(LB_ap1,xaxis='time', yaxis='GainAmp',iteraxis='spw')

""" Print calibration png file """
plotms(LB_ap1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_LB_gain_ap1_phase_vs_time.png'))
plotms(LB_ap1,xaxis='time', yaxis='GainAmp',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_scaled_selfcal_folder,f'{prefix}_ap1_LB_gain_amp_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_ap0+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_ap1], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_ap1 = LB_cont_ap0.replace('ap0','ap1')
os.system('rm -rf %s.ms*' % LB_cont_ap1)
split(vis=LB_cont_ap0+'.ms', outputvis=LB_cont_ap1+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_ap1+'.ms', imagename = LB_cont_ap1,threshold = '0.028mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_ap1+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_ap1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_scaled_selfcal_folder)
#CQ_Tau_scaled_SBLB_contap1.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.23 deg)
#Flux inside disk mask: 438.85 mJy
#Peak intensity of source: 13.79 mJy/beam
#rms: 2.54e-02 mJy/beam
#Peak SNR: 543.42



#Now check the flux scaling

self_caled_LB_visibilities = {'p1':LB_cont_p1,
                              'p2':LB_cont_p2,
                              'p3':LB_cont_p3,
                              'ap0':LB_cont_ap0,
                              'ap1':LB_cont_ap1}

for vis in self_caled_LB_visibilities.values(): 
    listobs(vis=vis+'.ms',listfile=vis+'.ms.txt',overwrite=True)

LB_EBs = ('EB0','EB1','EB2','EB3')
LB_EB_spws = ('0,1,2,3','4,5,6,7','8,9,10,11','12,13,14,15') #fill out by referring to listobs output

for self_cal_step,self_caled_vis in self_caled_LB_visibilities.items():
    for EB_key,spw in zip(LB_EBs,LB_EB_spws):
        nametemplate = f'{prefix}_LB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',LB_cont_p0+'.ms']
        print(visibilities)
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['LB'],output_folder=LB_scaled_selfcal_folder)

#set to the EB of the combined SBLB data that corresponds to flux_ref_EB
SBLB_flux_ref_EB = 3 #this is LB EB3

for self_cal_step,vis_name in self_caled_LB_visibilities.items():
    vis_ms = vis_name+'.ms'
    nametemplate = vis_ms.replace('.ms','_EB')
    split_all_obs(msfile=vis_ms,nametemplate=nametemplate)
    for i in range(total_number_of_EBs):
        export_MS(f'{nametemplate}{i}.ms')
    for i in range(total_number_of_EBs):
        reference = f'{nametemplate}{SBLB_flux_ref_EB}.vis.npz'
        output = f'flux_comparison_EB{i}_to_EB{SBLB_flux_ref_EB}'\
                       +f'_SBLB_{self_cal_step}.png'
        plot_label = os.path.join(LB_scaled_selfcal_folder,output)
        estimate_flux_scale(reference=reference,
                            comparison=f'{nametemplate}{i}.vis.npz',
                            incl=incl, PA=PA,uvbins = np.arange(40.,300.,20.),
                            plot_label=plot_label)
    filelist = [f'{nametemplate}{i}.vis.npz' for i in range(total_number_of_EBs)]
    fluxscale = [1.,]*total_number_of_EBs
    plot_label = os.path.join(LB_scaled_selfcal_folder,
                              f'deprojected_vis_profiles_SBLB_{self_cal_step}.png')
    plot_deprojected(filelist=filelist,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)
#after phase-only selfcal:

#Measurement set exported to CQ_Tau_scaled_SBLB_contp3_EB5.vis.npz
#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB0.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 0.96455
#The scaling factor for gencal is 0.982 for your comparison measurement
#The error on the weighted mean ratio is 4.227e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB1.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 1.01702
#The scaling factor for gencal is 1.008 for your comparison measurement
#The error on the weighted mean ratio is 4.133e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB2.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 1.03796
#The scaling factor for gencal is 1.019 for your comparison measurement
#The error on the weighted mean ratio is 3.957e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 1.00000
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 3.496e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB4.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 0.99593
#The scaling factor for gencal is 0.998 for your comparison measurement
#The error on the weighted mean ratio is 2.805e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contp3_EB5.vis.npz to
#CQ_Tau_scaled_SBLB_contp3_EB3.vis.npz is 0.99924
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 3.401e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor
    

#after amp selfcal:

#Measurement set exported to CQ_Tau_scaled_SBLB_contap1_EB5.vis.npz
#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB0.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 0.99558
#The scaling factor for gencal is 0.998 for your comparison measurement
#The error on the weighted mean ratio is 4.363e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB1.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 0.99992
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 4.061e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB2.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 0.99846
#The scaling factor for gencal is 0.999 for your comparison measurement
#The error on the weighted mean ratio is 3.803e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 1.00000
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 3.495e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB4.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 1.00288
#The scaling factor for gencal is 1.001 for your comparison measurement
#The error on the weighted mean ratio is 2.824e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of CQ_Tau_scaled_SBLB_contap1_EB5.vis.npz to
#CQ_Tau_scaled_SBLB_contap1_EB3.vis.npz is 0.99232
#The scaling factor for gencal is 0.996 for your comparison measurement
#The error on the weighted mean ratio is 3.373e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


############ END of SB self-cal 2 ############

""" Split out final continuum ms table, with a 30s timebin
"""

LB_cont_averaged = f'{prefix}_time_ave_continuum'
os.system(f'rm -rf {LB_cont_averaged}.ms*')
split(vis=LB_cont_ap1+'.ms', outputvis=LB_cont_averaged+'.ms', datacolumn='data',
      keepflags=False, timebin='30s')


"""
Now apply these solutions to the line data
"""

calibrate_linedata_folder = get_figures_folderpath('12_apply_cal_to_lines')
make_figures_folder(calibrate_linedata_folder)

""" Check that lines are not flagged in not averaged data"""
for params in data_params.values():
    plotfile = os.path.join(
                calibrate_linedata_folder,
                f'{prefix}_{params["name"]}_chan-v-amp_preselfcal_after_flagging.png')
    plotms(vis=params['vis'],
           xaxis='channel',
           yaxis='amplitude',
           field=params['field'],
           ydatacolumn='data',
           avgtime='1e8',
           avgscan=True,
           avgbaseline=True,
           coloraxis='corr',
           iteraxis='spw',
           showgui = False,
           exprange='all',
           plotfile=plotfile)


""" Apply gaintables of individual EBs"""
for params in data_params.values():
    single_EB_p1 = f'{prefix}_{params["name"]}_initcont.p1'
    vis = f'{prefix}_{params["name"]}.ms'
    applycal(vis=vis,spw='0~3',spwmap=[0,0,0,0],gaintable=[single_EB_p1],
             interp='linearPD',applymode='calonly',calwt=True)
    split(vis=vis,outputvis=vis[:-3]+'_no_ave_selfcal.ms',datacolumn='corrected')

### ALIGN DATA ###
# we re-align the no_ave data, as we have done for the "initcont" ms tables.
# We go from *_no_ave_selfcal.ms to *_no_ave_shift.ms

reference_ms = {'LB':reference_for_LB_alignment,
                'SB':reference_for_SB_alignment}
for params in data_params.values():
    unshifted_ms = f'{prefix}_{params["name"]}_no_ave_selfcal.ms'
    array_key,_ = params['name'].split('_') #LB or SB
    offset = alignment_offsets[params['name']]
    #npix and cell_size are not needed because we do not fit any offset
    alignment.align_measurement_sets(
            reference_ms=reference_ms[array_key],align_ms=[unshifted_ms],
            align_offsets=[offset],npix=None,cell_size=None)

### END OF ALIGN DATA ###


"""
re-scale the shifted *no_ave* EBs
"""

for i in range(number_of_EBs['SB']):
    scaling_factor = data_params[f'SB{i}']['flux_scaling_factor']
    if scaling_factor is None:
        print(f'no scaling, will just copy SB EB{i}')
        shutil.copytree(src=f'{prefix}_SB_EB{i}_no_ave_selfcal_shift.ms',
                        dst=f'{prefix}_SB_EB{i}_no_ave_selfcal_shift_rescaled.ms')
    else:
        print(f'going to rescale SB EB{i}')
        rescale_flux(vis=f'{prefix}_SB_EB{i}_no_ave_selfcal_shift.ms',
                     gencalparameter=[scaling_factor])


""" Concat not averaged SB data """
SB_combined = f'{prefix}_SB_no_ave_concat'
os.system('rm -rf %s.ms*' % SB_combined)
concat(vis=[f'{prefix}_SB_EB{i}_no_ave_selfcal_shift_rescaled.ms' for i
            in range(number_of_EBs['SB'])],
       concatvis=SB_combined+'.ms', dirtol='0.1arcsec', copypointing=False)

listobs(vis=SB_combined+'.ms',listfile=SB_combined+'.ms.txt',overwrite=True)

#be careful here to use the right gain tables (those from the second round)
applycal(vis=SB_combined+'.ms', spw='0~7',
         gaintable=[SB_p1,SB_p2,SB_p3,SB_p4,SB_p5],
         spwmap = [SB_spw_mapping]*5,interp=['linearPD']*5, calwt=True,
         applymode='calonly',flagbackup=False)

SB_no_ave_selfcal = f'{prefix}_SB_no_ave_selfcal.ms'
os.system(f'rm -rf {SB_no_ave_selfcal}*')
split(vis=SB_combined+'.ms', outputvis=SB_no_ave_selfcal,datacolumn='corrected')


""" Rescale not averaged LB data """
for i in range(number_of_EBs['LB']):
    scaling_factor = data_params[f'LB{i}']['flux_scaling_factor']
    if scaling_factor is None:
        print(f'no scaling, will just copy LB EB{i}')
        shutil.copytree(src=f'{prefix}_LB_EB{i}_no_ave_selfcal_shift.ms',
                        dst=f'{prefix}_LB_EB{i}_no_ave_selfcal_shift_rescaled.ms')
    else:
        print(f'rescaling LB EB{i}')
        rescale_flux(vis=f'{prefix}_LB_EB{i}_no_ave_selfcal_shift.ms',
                     gencalparameter=[scaling_factor])

""" Concat not averaged LB data """

LB_combined = f'{prefix}_SBLB_no_ave_concat.ms'
os.system(f'rm -rf {LB_combined}*')
concat(vis=[SB_no_ave_selfcal]+[f'{prefix}_LB_EB{i}_no_ave_selfcal_shift_rescaled.ms' for i
                                in range(number_of_EBs['LB'])],
       concatvis=LB_combined, dirtol='0.1arcsec', copypointing=False)

listobs(vis=LB_combined,listfile=LB_combined+'.txt',overwrite=True)

#again carefule here to apply the gain tables from the second round
applycal(vis=LB_combined, spw='0~23',
         gaintable=[LB_p1,LB_p2,LB_p3,LB_ap0,LB_ap1],
         spwmap = [LB_spw_mapping]*5,interp=['linearPD']*5,
         calwt=True, applymode='calonly',flagbackup=False)

SBLB_no_ave_selfcal = f'{prefix}_SBLB_no_ave_selfcal_time_ave.ms'
os.system(f'rm -rf {SBLB_no_ave_selfcal}*')
""" time average of 30s, tests show there is no different with data without time average """
split(vis=LB_combined, outputvis=SBLB_no_ave_selfcal,
      datacolumn='corrected', timebin = '30s', keepflags=False)

listobs(vis=SBLB_no_ave_selfcal,listfile=SBLB_no_ave_selfcal+'.txt',overwrite=True)

# Check that solutions have been applied correctly by flagging the line data, averaging and imaging continuum
# continuum has to be the same imaged in the last step of the self-cal
complete_dataset_dict = {'vis' : SBLB_no_ave_selfcal,
                         'name' : 'SBLB_concat',
                         'field' : fields['LB'],
                         'line_spws': np.array([0,2,3,
                                                4,6,7,
                                                8,10,11,
                                                12,14,15,
                                                16,18,19,
                                                20,22,23]), # list of spws containing lines
                         'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]*6), #frequencies (Hz) corresponding to line_spws
                         'cont_spws': None,
                         'width_array': None,
                         }

#use the output of get_flagchannels at the beginning of the script to define fitspw:
# Flagchannels input string for LB_EB0: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB1: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB2: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for LB_EB3: '0:836~3004, 2:793~3042, 3:788~3056'
# Flagchannels input string for SB_EB0: '0:833~3001, 2:797~3046, 3:780~3048'
# Flagchannels input string for SB_EB1: '0:833~3001, 2:797~3046, 3:781~3048'

fitspw =  '0:836~3004, 1:0, 2:793~3042, 3:788~3056,'\
         +'4:836~3004, 5:0, 6:793~3042, 7:788~3056,'\
         +'8:836~3004, 9:0, 10:793~3042, 11:788~3056,'\
         +'12:836~3004, 13:0, 14:793~3042, 15:788~3056,'\
         +'16:833~3001, 17:0, 18:797~3046, 19:780~3048,'\
         +'20:833~3001, 21:0, 22:797~3046, 23:781~3048'

avg_cont(ms_dict=complete_dataset_dict, output_prefix=prefix, flagchannels = fitspw,
         contspws = complete_dataset_dict['cont_spws'],
         width_array=complete_dataset_dict['width_array'])

# image the "avg then cal" with same parameters as in last step of self-cal
tclean_wrapper(vis=LB_cont_averaged+'.ms',
               imagename = LB_cont_averaged+'_image', mask=LB_mask,
               threshold = '0.028mJy', deconvolver='multiscale', scales=scales['LB'],
               imsize=imsize['LB'], cellsize=cellsize['LB'],
               robust=0.5, interactive=False, parallel=use_parallel, gridder='standard')
estimate_SNR(LB_cont_averaged+'_image'+'.image', disk_mask = LB_mask,
             noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_averaged+'_image'+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=calibrate_linedata_folder)
#CQ_Tau_time_ave_continuum_image.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.23 deg)
#Flux inside disk mask: 438.83 mJy
#Peak intensity of source: 13.79 mJy/beam
#rms: 2.54e-02 mJy/beam
#Peak SNR: 543.36

# image the "cal then avg" with the same parameters as in last step of self-cal
complete_dataset_image = prefix+'_'+complete_dataset_dict['name']+'_initcont_image'
tclean_wrapper(vis=prefix+'_'+complete_dataset_dict['name']+'_initcont.ms',
               imagename = complete_dataset_image, mask=LB_mask,
               threshold = '0.028mJy', deconvolver='multiscale', scales=scales['LB'],
               imsize=imsize['LB'], cellsize=cellsize['LB'],
               robust=0.5, interactive=False, parallel=use_parallel, gridder='standard')
estimate_SNR(complete_dataset_image+'.image', disk_mask = LB_mask,
             noise_mask = noise_annulus_LB)
generate_image_png(complete_dataset_image+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=calibrate_linedata_folder)
#CQ_Tau_SBLB_concat_initcont_image.image
#Beam 0.111 arcsec x 0.077 arcsec (-29.23 deg)
#Flux inside disk mask: 438.86 mJy
#Peak intensity of source: 13.79 mJy/beam
#rms: 2.54e-02 mJy/beam
#Peak SNR: 543.27

# Plot ratio of "cal then avg" and "avg then cal". It should be equal to ~one (only difference being the time average):
ref_image = LB_cont_averaged+'_image'+'.image'
os.system('rm -rf '+complete_dataset_image+'.ratio')
immath(imagename=[ref_image,complete_dataset_image+'.image'],mode='evalexpr',
       outfile=complete_dataset_image+'.ratio',
       expr='iif(IM0 > 3*'+str(rms_LB)+', IM1/IM0, 0)')
generate_image_png(f'{complete_dataset_image}.ratio',
                   plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                   color_scale_limits=[0.5,1.5],image_units='ratio',
                   save_folder=calibrate_linedata_folder)


# Do continuum subtraction
contsub_vis = f'{SBLB_no_ave_selfcal}.contsub'
os.system(f'rm -rf {contsub_vis}*')
uvcontsub(vis=SBLB_no_ave_selfcal, spw='0~23', fitspw=fitspw,
          excludechans=True, solint='int', fitorder=1, want_cont=False)

# Split final ms table into separate spws for 12CO, 13CO, CS and continuum

#12CO
vis_12CO = SBLB_no_ave_selfcal[:-3]+'_12CO.ms'
os.system(f'rm -rf {vis_12CO}*')
spw_12CO = '3,7,11,15,19,23'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_12CO,spw=spw_12CO,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_12CO}.contsub',spw=spw_12CO,
      datacolumn='data', keepflags=False)

#13CO
vis_13CO = SBLB_no_ave_selfcal[:-3]+'_13CO.ms'
os.system(f'rm -rf {vis_13CO}*')
spw_13CO = '0,4,8,12,16,20'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_13CO,spw=spw_13CO,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_13CO}.contsub',spw=spw_13CO,
      datacolumn='data', keepflags=False)

#CS
vis_CS = SBLB_no_ave_selfcal[:-3]+'_CS.ms'
os.system(f'rm -rf {vis_CS}*')
spw_CS = '2,6,10,14,18,22'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_CS,spw=spw_CS,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_CS}.contsub',spw=spw_CS,
      datacolumn='data', keepflags=False)

#continuum spw
vis_continuum = SBLB_no_ave_selfcal[:-3]+'_contspw.ms'
os.system(f'rm -rf {vis_continuum}*')
spw_continuum = '1,5,9,13,17,21'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_continuum,spw=spw_continuum,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_continuum}.contsub',spw=spw_continuum,
      datacolumn='data', keepflags=False)

for vis in (vis_12CO,vis_13CO,vis_CS):
    listobs(vis=f'{vis}.contsub',listfile=f'{vis}.contsub.txt',overwrite=True)
listobs(vis=vis_continuum,listfile=f'{vis_continuum}.txt',overwrite=True)